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Catchpoint Announces Active Observers for Mobile Edge Compute and AWS Wavelength

Catchpoint announced support for the active observability of mobile edge and AWS Wavelength zones.

With a plan to add further edge compute locations and providers in the future, ultimately creating a separate edge compute observability network, these data sources are a future-proofed enhancement to Catchpoint’s already industry-leading observability network. The new edge observability data sources will arm DevOps teams with the visibility to deliver reliable experiences at the 5G mobile edge, software developers with the insights necessary to optimize their applications for mobile edge compute, and businesses with the ability to innovate at the speed of the ever-growing mobile user base.

“Mobile Edge and 5G are transforming the mobile internet experience for consumers and businesses,” says Mehdi Daoudi, CEO of Catchpoint. “With our new observational data sources, service and application providers will get the trust and confidence they need to protect their brand and business investments. And IT technologists will get the reliability and innovation capabilities they demand to deliver amazing digital experiences in ways only now possible because of the 5G mobile edge.”

These new data sources are strategically placed at the mobile carrier edge to expand access to valuable baselining, benchmarking, and monitoring data. Since DevOps, site reliability, and platform engineers and any monitoring strategist will gain insight into the performance and reliability of edge delivery services without the variability of over-the-air noise ratios, they will be able to power a new set of innovative business use cases. These include rich content delivery for wireless infrastructure stadiums, remote compute for low-power IoT devices, the ability to stream smooth content to global audiences or lag-free games for the most demanding gaming experiences.

"5G, often in combination with mobile edge computing, enables enhanced mobile broadband, massive machine-type internet-of-things communications, and ultrareliable low-latency communications," wrote Dan Bieler, Principal Analyst at Forrester¹

Traditional agent-based APM or system-centric monitoring have no monitoring reach at the edge; customers are unable to use them to directly make active observations that can address these new emerging use cases. AWS Wavelength is an AWS infrastructure offering optimized for mobile edge computing (MEC) applications. It embeds AWS compute and storage services within communications service providers’ datacenters at the edge of the 5G network. Combined with Catchpoint’s digital observability platform, their customers can meet evolving, unrelenting user expectations, which are causing businesses to continually move compute closer to the edge and take advantage of offerings such as Wavelength. These new added data sources are a part of Catchpoint’s continual fulfillment of future-proofing observability strategies to preclude the need for businesses to have to search for new or additional vendors.

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Catchpoint Announces Active Observers for Mobile Edge Compute and AWS Wavelength

Catchpoint announced support for the active observability of mobile edge and AWS Wavelength zones.

With a plan to add further edge compute locations and providers in the future, ultimately creating a separate edge compute observability network, these data sources are a future-proofed enhancement to Catchpoint’s already industry-leading observability network. The new edge observability data sources will arm DevOps teams with the visibility to deliver reliable experiences at the 5G mobile edge, software developers with the insights necessary to optimize their applications for mobile edge compute, and businesses with the ability to innovate at the speed of the ever-growing mobile user base.

“Mobile Edge and 5G are transforming the mobile internet experience for consumers and businesses,” says Mehdi Daoudi, CEO of Catchpoint. “With our new observational data sources, service and application providers will get the trust and confidence they need to protect their brand and business investments. And IT technologists will get the reliability and innovation capabilities they demand to deliver amazing digital experiences in ways only now possible because of the 5G mobile edge.”

These new data sources are strategically placed at the mobile carrier edge to expand access to valuable baselining, benchmarking, and monitoring data. Since DevOps, site reliability, and platform engineers and any monitoring strategist will gain insight into the performance and reliability of edge delivery services without the variability of over-the-air noise ratios, they will be able to power a new set of innovative business use cases. These include rich content delivery for wireless infrastructure stadiums, remote compute for low-power IoT devices, the ability to stream smooth content to global audiences or lag-free games for the most demanding gaming experiences.

"5G, often in combination with mobile edge computing, enables enhanced mobile broadband, massive machine-type internet-of-things communications, and ultrareliable low-latency communications," wrote Dan Bieler, Principal Analyst at Forrester¹

Traditional agent-based APM or system-centric monitoring have no monitoring reach at the edge; customers are unable to use them to directly make active observations that can address these new emerging use cases. AWS Wavelength is an AWS infrastructure offering optimized for mobile edge computing (MEC) applications. It embeds AWS compute and storage services within communications service providers’ datacenters at the edge of the 5G network. Combined with Catchpoint’s digital observability platform, their customers can meet evolving, unrelenting user expectations, which are causing businesses to continually move compute closer to the edge and take advantage of offerings such as Wavelength. These new added data sources are a part of Catchpoint’s continual fulfillment of future-proofing observability strategies to preclude the need for businesses to have to search for new or additional vendors.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...